Humans spend much of their time engaging in activities that are pleasurable in their own right. These activities are undertaken even when they do not lead to external outcomes: they are intrinsically rewarding. Watching the sunset, reading, solving cross word puzzles, playing, exploring nature, observing works of art, are a few such examples.
On the surface these different activities do not have common features or goals. This contrasts with activities that lead to primary rewards (e.g., eating, fornicating), which all have clear and direct survival benefits, and secondary rewards (e.g., money), which in turn are associated with primary rewards. It is possible, however, that different intrinsic rewards do share core characteristics, mechanisms, and goals not readily transparent. If so, such common features should elicit similar types of behavioural responses, and individual differences in these responses should be partially domain general. Here, we test this hypothesis. Namely, that despite diverse intrinsic rewards seeming vastly different from each other, sensitivity to them is partially domain general and may be shared with secondary rewards.
Engagement with specific intrinsically rewarding stimulus has been associated with happiness, mental health, and professional achievement. Here, we pose that these past findings can in fact be explained by a core association between mental health and domain-general sensitivity to intrinsic rewards. That is, if an individual finds a specific stimulus rewarding (e.g. observing landscapes), they may be more likely to find other stimuli (e.g. reading, playing, etc.) rewarding due to a domain-general sensitivity to (intrinsic) rewards, which may be associated with mental health. Individuals with high sensitivity to intrinsic rewards will be inclined to engage with a variety of seemingly diverse intrinsically rewarding activities, which in turn will increase the likelihood that they will eventually find rewarding activities that they also excel at. Low sensitivity to intrinsic rewards, on the other hand, will produce a general disinterest in a large variety of activities, which will lead to low mood and lack of motivation. Thus, a domain-general sensitivity to intrinsic rewards will contribute to flourishing and its absence to suffering.
We focus here on a core aspect of mental health which we will refer to as ‘affective health’. We define ‘affective health’ as a range of characteristics that are related to positive mood, high motivation, feelings of pleasure, interest, and happiness. To investigate whether sensitivity to intrinsic rewards is domain general and related to affective aspects of mental health, we selected three putative intrinsically rewarding** stimuli from the visual domain, cognitive domain, and social domain, (Fig 1A). We also compared responses to these putative intrinsic rewards with responses to monetary reward, to test whether sensitivity to intrinsic rewards is shared with that to secondary rewards.
It has been suggested that a stimulus is a ‘reward’ if it elicits three typical responses. First, it elicits positive emotions (it is ‘liked’). Second, it generates approach/consummatory behaviours (it is ‘wanted’). Third, it increases the likelihood of the action that led to it (it is ‘reinforcing’). Thus, we measured liking, wanting and reinforcement of all stimuli to assess individuals’ reward sensitivity. We also asked participants to fill a range of questionnaires related to affective aspects of mental health and implemented a dimensionality approach, which considers the possibility that specific symptoms is predictive of several conditions, thus allowing an investigation that cuts through classic clinical boundaries. Together, the data allowed us to examine if within-individual responses to intrinsic rewards are domain general and linked to mental health.
# power analysis
pwr.r.test(r=0.25,power=0.80,sig.level=0.05,alternative="two.sided")
##
## approximate correlation power calculation (arctangh transformation)
##
## n = 122.4466
## r = 0.25
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
# Demo
print(lDemo1 %>%
mutate(gender = ifelse(is.na(gender), "Unknown", ifelse(gender == 0, "Female", "Male"))) %>%
group_by(gender) %>%
summarize(
Proportion = n() / nrow(lDemo1),
Average_Age = mean(age, na.rm = TRUE),
SD_Age = sd(age, na.rm = TRUE),
Average_IQ = mean(iq, na.rm = TRUE),
SD_IQ = sd(iq, na.rm = TRUE),
Average_Edu = mean(edu, na.rm = TRUE),
SD_Edu = sd(edu, na.rm = TRUE),
Average_Married = mean(married, na.rm = TRUE),
SD_Married = sd(married, na.rm = TRUE)
))
## # A tibble: 2 × 10
## gender Proportion Averag…¹ SD_Age Avera…² SD_IQ Avera…³ SD_Edu Avera…⁴ SD_Ma…⁵
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Female 0.341 33.0 12.0 7.91 3.31 3.31 1.55 0.356 0.484
## 2 Male 0.659 28.7 9.16 8.79 3.46 3.03 1.60 0.391 0.491
## # … with abbreviated variable names ¹Average_Age, ²Average_IQ, ³Average_Edu,
## # ⁴Average_Married, ⁵SD_Married
# power analysis
pwr.r.test(r=0.22,power=0.80,sig.level=0.05,alternative="two.sided")
##
## approximate correlation power calculation (arctangh transformation)
##
## n = 159.0316
## r = 0.22
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
# Demo
print(lDemo2 %>%
mutate(gender = ifelse(is.na(gender), "Unknown", ifelse(gender == 0, "Female", "Male"))) %>%
group_by(gender) %>%
summarize(
Proportion = n() / nrow(lDemo2),
Average_Age = mean(age, na.rm = TRUE),
SD_Age = sd(age, na.rm = TRUE),
Average_IQ = mean(iq, na.rm = TRUE),
SD_IQ = sd(iq, na.rm = TRUE),
Average_Edu = mean(edu, na.rm = TRUE),
SD_Edu = sd(edu, na.rm = TRUE),
Average_Married = mean(married, na.rm = TRUE),
SD_Married = sd(married, na.rm = TRUE)
))
## # A tibble: 2 × 10
## gender Proportion Averag…¹ SD_Age Avera…² SD_IQ Avera…³ SD_Edu Avera…⁴ SD_Ma…⁵
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Female 0.509 33.7 15.6 7.25 3.31 2.98 1.49 0.391 0.491
## 2 Male 0.491 34.8 12.8 8.29 2.99 3.11 1.70 0.369 0.485
## # … with abbreviated variable names ¹Average_Age, ²Average_IQ, ³Average_Edu,
## # ⁴Average_Married, ⁵SD_Married
# power analysis
pwr.r.test(r=0.22,power=0.80,sig.level=0.05,alternative="two.sided")
##
## approximate correlation power calculation (arctangh transformation)
##
## n = 159.0316
## r = 0.22
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
# Demo
print(lDemo3 %>%
mutate(gender = ifelse(is.na(gender), "Unknown", ifelse(gender == 0, "Female", "Male"))) %>%
group_by(gender) %>%
summarize(
Proportion = n() / nrow(lDemo3),
Average_Age = mean(age, na.rm = TRUE),
SD_Age = sd(age, na.rm = TRUE),
Average_IQ = mean(iq, na.rm = TRUE),
SD_IQ = sd(iq, na.rm = TRUE),
Average_Edu = mean(edu, na.rm = TRUE),
SD_Edu = sd(edu, na.rm = TRUE),
Average_Married = mean(married, na.rm = TRUE),
SD_Married = sd(married, na.rm = TRUE)
))
## # A tibble: 2 × 10
## gender Proportion Averag…¹ SD_Age Avera…² SD_IQ Avera…³ SD_Edu Avera…⁴ SD_Ma…⁵
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Female 0.406 38.4 11.7 7.93 3.37 3.32 1.64 0.562 0.500
## 2 Male 0.594 38.7 11.6 8.43 3.69 3.29 1.73 0.589 0.494
## # … with abbreviated variable names ¹Average_Age, ²Average_IQ, ³Average_Edu,
## # ⁴Average_Married, ⁵SD_Married
For all experiments, ethical approval was provided by the Research Ethics Committee at University College London (Project ID Number:3990/003) and all participants gave written informed consent to participate.
Participants (Experiment 1). Based on a pilot study we estimated an effect size of about 0.25. Thus, for a regression with a power of 80% and alpha = 0.05, we needed a sample size of 128. We added 15% to account for failed attention checks, which resulted in 149 participants. Data were collected between November 22nd , 2021 and November 29th, 2021.
One hundred and fourty nine participants completed the task on Prolific (https://www.prolific.co/) online system. 17 participants failed the comprehension and/or attention checks, thus their data was not analysed (see details below). Thus, data of 132 participants were analysed (female = 34%, age = 33 ± 12 (M ± SD); male = 66%, age = 29 ± 9; other = 0%). Participants received £7.50 per hour for their participation plus a 50p “bonus” payment. The experiment lasted for about 90 minutes. For all experiments, ethical approval was provided by the Research Ethics Committee at University College London and all participants gave written informed consent to participate.
Participants (Experiment 2). Sample size was based on a power analysis based on Experiment 1, which showed that 159 participants were required for a regression weight of 0.22 (lower bound of the effect size from the regression between mental health score and reward sensitivity, without correcting for demographics, which was 0.23 ± 0.01 in Experiment 1) with 80% power and alpha = 0.05. We anticipated that about 30 participants will fail the attention and/or comprehension checks. Therefore, we recruited 188 participants on the Prolific website. Data of 17 participants were not analysed as they did not pass the comprehension and/or attention checks. Thus, 171 participant’s data was analysed (female = 51%, age = 34 ± 16 (M ± SD); male = 49%, age = 35 ± 13; other = 0%). Participants received £7.50 per hour for their participation. Data were collected between January 20th 2022 and January 25th 2022.
Participants (Experiment 3). Data was collected between January 18th 2023 and January 31st 2023. Sample size was calculated as for Experiment 2. As we added more catch trials (in the wanting measure, see below), we expected a higher rate of failure at the attention checks. Therefore, we added about an extra 40 participants and we therefore recruited 198 participants on Prolific website. Data of 18 participants was not analysed as they did not pass comprehension and/or attention checks. Thus, 180 participant’s data was analysed (female = 41%, age = 38 ± 12 (M ± SD); male = 59%, age = 39 ± 12; other = 0%).
Two identical online studies were conducted (Experiment 1: N = 132, Experiment 2: N = 171), as well as a modified version (Experiment 3: N = 180). In each we measured three types of responses: liking, wanting, and reinforcing strength (in that order in Experiment 1&2, and counterbalanced in Experiment 3, see Methods) to ten categories of stimuli. There were three categories of putative intrinsic reward (visual, cognitive, social) and three categories of non-rewarding alternative stimuli (visual, cognitive, social). In the visual domain, we used landscapes as a reward and images of walls as an alternative (Figure 1A & Methods). Staring at landscapes has been rated positively in the past unlike looking at walls7,17. In the cognitive domain, we presented participants with trivia facts, as consuming such information (e.g. reading) is thought to be rewarding9,10. It has been shown that humans select to observe (and thus read) sentences and that the opportunity to consume sentences that increase general knowledge activates the reward system just like primary rewards, suggesting that consuming knowledge is rewarding9,10. We presented random string of letters as an alternative. In the social domain, we used social similarity as a reward (i.e. a participant learns that another participant shares the same preference as them) and social disagreement as an alternative. This was selected as studies have shown the existence of a confirmation bias by which subjects select to observe information that they suspect confirms their believes (e.g. 18) including in the social domain11. Moreover, confirmation has been shown to activate the reward system 11, suggesting that consuming confirmatory information (e.g., learning that someone agrees with you) is rewarding. The monetary reward was earning bonus money (represented on screen as a coin) and its alternative was not receiving a coin. All reward types were compared to a neutral stimulus: vertical and horizontal lines.
To measure liking, participants were exposed to a block of a rewarding stimulus and to a block of its alternative and were asked to report how much they liked that block after each block (Figure 1B & Methods). To measure wanting in Experiment 1 & 2, participants indicated whether they preferred to experience ten trials of the reward or ten trials of its control on a sliding preference scale, which deterministically and explicitly led to the presentation of either ten trials of the putative intrinsically rewarding stimulus or its alternative. In Experiment 3, on each of 15 trials participants chose whether to be exposed to a rewarding stimulus or to its alternative and their choice was immediately honoured. To measure reinforcing strength, that is whether a stimulus increase the likelihood of the action that preceded them, participants chose between abstract shapes probabilistically linked to the putative intrinsically rewarding stimulus or to the corresponding alternative.
Figure 1.
Experimental design. A: Stimuli included five categories –visual,
cognitive, social and monetary rewards as well as neutral stimuli.
Intrinsic rewards were the visual reward (landscapes), the cognitive
reward (facts) and the social reward (social approbation of
participant’s preference which was collected at the beginning of the
experiment). The monetary reward was a coin signalling a bonus payment.
It was always the same coin in Experiment 1 & 2, but different coins
on every trial in Experiment 3. Each reward was associated with an
alternative stimulus. This included walls for the visual reward, a
random string of letters for the cognitive reward, disagreement for the
social reward, and not receiving a coin for the secondary reward.
Neutral stimuli were vertical and horizontal lines. B: For each reward
type, participants were exposed to two blocks of the rewarding stimulus
(five trials each) and two blocks of the alternative stimulus (five
trials each). Order was either reward-alternative-reward-alternative or
alternative-reward-alternative-reward. After each block participants
rated how much they liked that block. To measure wanting in Experiment 1
& 2, participants were asked to rate how much they wanted to be
exposed to 10 trials of the rewarding stimulus or 10 trials of the
alternative stimulus. They were then exposed to ten trials of the chosen
stimulus. In Experiment 3, on each of 15 trials participants chose
whether to be exposed to a rewarding stimulus or the alternative
stimuli, and then their choice was honoured. To measure the reinforcing
strength of each reward, participants were exposed to a pair of abstract
cues each probabilistically related to the rewarding stimulus with
either 0.75 probability or 0.25 probability and to the alternative
stimulus with either 0.25 or 0.75 probability. The percentage of trials
in which they selected the abstract cue leading more often to the
rewarding stimulus was the measure of the reinforcing strength. Each
reward type was presented in counterbalanced order across participants
for all experiments. In Experiment 1 & 2, the order was always
liking-wanting-reinforcement, while in Experiment 3 the order was
counterbalanced across participants and reward type. In this figure we
use landscapes (L) and walls (W) as example trials.
## Stimulus Avg_Reward_Rating SE_Reward_Rating Avg_Alternative_Rating
## LikingVR Visual 83.00000 1.484304 42.09848
## LikingCR Cognitive 79.38636 1.380679 22.95455
## LikingSR Social 70.48864 1.504691 47.84848
## LikingMR Monetary 88.71212 1.432357 15.34091
## LikingNV Neutral 55.45076 1.764459 52.64394
## SE_Alternative_Rating Diff_Avg SE_Diff t_value df p_value
## LikingVR 1.857563 40.901515 2.377750 17.572964 131 2.919813e-36
## LikingCR 1.629195 56.431818 2.135544 22.887482 131 1.302216e-47
## LikingSR 1.607248 22.640152 2.201668 11.304914 131 4.211114e-21
## LikingMR 1.618956 73.371212 2.161635 27.249024 131 8.043217e-56
## LikingNV 1.856676 2.806818 2.561359 2.342742 131 2.064910e-02
## Wall Letters Disconfirmation NoCoin Horizontal
## Landscape 40.9±2.3 60±2.3 35.2±2.1 67.7±2.3 30.4±2.2
## Information 37.3±2.3 56.4±2.5 31.5±2.2 64±2.2 26.7±2.1
## Confirmation 28.4±2.3 47.5±2.3 22.6±2 55.1±2.5 17.8±2
## Coin 46.6±2.3 65.8±2.4 40.9±2.1 73.4±2.7 36.1±2.2
## Vertical 13.4±2.3 32.5±2.2 7.6±2.3 40.1±2.4 2.8±1.2
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.919813e-36 2.299872e-53 1.654064e-33 2.890882e-59 8.000632e-27
## [2,] 4.994807e-33 1.302216e-47 2.106869e-28 7.956008e-59 9.432154e-25
## [3,] 2.784496e-24 1.624439e-43 4.211114e-21 1.309487e-46 1.776654e-15
## [4,] 1.130778e-41 9.794162e-56 1.074880e-40 8.043217e-56 6.072804e-33
## [5,] 7.214387e-08 3.436765e-30 1.522523e-03 7.194748e-35 2.064910e-02
## Reward (M) Reward (SE) t-value d.o.f.
## Visual 38.094696969697 2.56825139687677 14.8329314708146 131
## Cognitive 53.625 2.76874081797026 19.3680100542282 131
## Social 19.8333333333333 2.24459065659379 8.83605804696292 131
## Monetary 70.5643939393939 2.91032607110084 24.2462157900756 131
## p-value
## Visual 8.0233810713137e-30
## Cognitive 2.87779827153196e-40
## Social 5.65466760863075e-15
## Monetary 2.85595139958411e-50
## Stimulus Avg_Reward_Rating SE_Reward_Rating t_value df
## WantingV Visual 87.62121 1.8149892 20.7280639 131
## WantingC Cognitive 93.42424 1.4583080 29.7771409 131
## WantingS Social 75.56061 2.1638113 11.8127705 131
## WantingM Monetary 97.30303 0.8802942 53.7354811 131
## WantingN Neutral 50.76515 2.3049359 0.3319622 131
## p_value
## WantingV 3.476124e-43
## WantingC 3.586764e-60
## WantingS 2.255131e-22
## WantingM 4.044749e-91
## WantingN 7.404484e-01
## Stimulus Avg_Reward_Rating SE_Reward_Rating t_value df
## LearningV Visual 73.98990 1.936795 12.3863900 131
## LearningC Cognitive 76.60985 1.861475 14.2950338 131
## LearningS Social 68.84470 2.001720 9.4142529 131
## LearningM Monetary 77.71465 1.718057 16.1313893 131
## LearningN Neutral 51.86237 2.408677 0.7731937 131
## p_value
## LearningV 8.324974e-24
## LearningC 1.619087e-28
## LearningS 2.165395e-16
## LearningM 6.411397e-33
## LearningN 4.408007e-01
## Stimulus Avg_Reward_Rating SE_Reward_Rating Avg_Alternative_Rating
## LikingVR Visual 84.18713 1.239889 40.32456
## LikingCR Cognitive 76.04971 1.486423 20.98830
## LikingSR Social 68.08480 1.379409 46.70468
## LikingMR Monetary 87.42398 1.315437 16.84795
## LikingNV Neutral 50.84503 1.720045 50.46199
## SE_Alternative_Rating Diff_Avg SE_Diff t_value df p_value
## LikingVR 1.614309 43.8625731 2.035514 21.2959150 170 7.592334e-50
## LikingCR 1.517896 55.0614035 2.124491 25.2201437 170 2.420866e-59
## LikingSR 1.457379 21.3801170 2.006670 13.0594660 170 1.934553e-27
## LikingMR 1.494561 70.5760234 1.991002 29.3830564 170 1.589411e-68
## LikingNV 1.755835 0.3830409 2.457948 0.3516602 170 7.255286e-01
## Wall Letters Disconfirmation NoCoin Horizontal
## Landscape 43.9±2.1 63.2±2.2 37.5±1.9 67.3±2.1 33.7±2
## Information 35.7±2.2 55.1±2.2 29.3±1.9 59.2±2.1 25.6±2.3
## Confirmation 27.8±2.1 47.1±1.9 21.4±1.6 51.2±2 17.6±2.1
## Coin 47.1±2.2 66.4±2.2 40.7±2 70.6±2.4 37±2.2
## Vertical 10.5±2 29.9±2.1 4.1±2.1 34±2.2 0.4±1.1
## [,1] [,2] [,3] [,4] [,5]
## [1,] 7.592334e-50 3.387326e-68 5.007663e-46 1.529022e-74 5.268878e-37
## [2,] 1.094914e-36 2.420866e-59 4.221020e-33 6.765461e-65 1.245962e-21
## [3,] 2.042358e-28 7.604087e-57 1.934553e-27 6.495430e-59 2.325198e-14
## [4,] 1.123579e-50 1.917163e-69 3.015389e-46 1.589411e-68 1.555240e-38
## [5,] 2.733151e-07 5.805963e-31 4.723487e-02 1.001448e-34 7.255286e-01
## Reward (M) Reward (SE) t-value d.o.f.
## Visual 43.4795321637427 2.36042198831836 18.4202368809142 170
## Cognitive 54.6783625730994 2.36967722254049 23.0741816028766 170
## Social 20.9970760233918 1.75529322373021 11.962147258092 170
## Monetary 70.1929824561404 2.55620770923119 27.4598117369937 170
## p-value
## Visual 2.3336670168733e-42
## Cognitive 2.97154942625477e-54
## Social 2.56496197676211e-24
## Monetary 2.15799291865809e-64
## Stimulus Avg_Reward_Rating SE_Reward_Rating t_value df
## WantingV Visual 90.21637 1.4950094 26.9004156 170
## WantingC Cognitive 92.95906 1.3932452 30.8338145 170
## WantingS Social 75.15789 1.8532016 13.5753684 170
## WantingM Monetary 96.32749 0.8749399 52.9493352 170
## WantingN Neutral 50.78363 1.9817623 0.3954186 170
## p_value
## WantingV 3.723567e-63
## WantingC 1.592634e-71
## WantingS 6.581018e-29
## WantingM 1.436979e-107
## WantingN 6.930297e-01
## Stimulus Avg_Reward_Rating SE_Reward_Rating t_value df
## LearningV Visual 72.44152 1.706210 13.1528488 170
## LearningC Cognitive 74.82943 1.764743 14.0697171 170
## LearningS Social 65.78947 1.817050 8.6896214 170
## LearningM Monetary 76.92495 1.537947 17.5070719 170
## LearningN Neutral 51.36452 2.041660 0.6683397 170
## p_value
## LearningV 1.048794e-27
## LearningC 2.593948e-30
## LearningS 2.944070e-15
## LearningM 6.815603e-40
## LearningN 5.048232e-01
## Stimulus Avg_Reward_Rating SE_Reward_Rating Avg_Alternative_Rating
## LikingVR Visual 83.79722 1.143076 39.27778
## LikingCR Cognitive 78.11667 1.260604 16.30556
## LikingSR Social 74.55000 1.128252 39.80556
## LikingMR Monetary 71.38056 1.469240 17.21667
## LikingNV Neutral 47.12500 1.602585 45.13333
## SE_Alternative_Rating Diff_Avg SE_Diff t_value df p_value
## LikingVR 1.444393 44.519444 1.841980 21.929375 179 1.348753e-52
## LikingCR 1.308381 61.811111 1.816861 29.516721 179 1.096806e-70
## LikingSR 1.293201 34.744444 1.716194 18.056133 179 3.569080e-42
## LikingMR 1.526987 54.163889 2.119046 23.606884 179 7.317435e-57
## LikingNV 1.682751 1.991667 2.323775 1.226959 179 2.214493e-01
## Wall Letters Disconfirmation NoCoin Horizontal
## Landscape 44.5±2 67.5±2 44±1.9 66.6±2.2 38.7±2
## Information 38.8±2 61.8±2.1 38.3±2 60.9±2.2 33±2
## Confirmation 35.3±2 58.2±2 34.7±1.9 57.3±2.1 29.4±1.9
## Coin 32.1±2.1 55.1±2.1 31.6±2.1 54.2±2.3 26.2±2.2
## Vertical 7.8±2 30.8±1.9 7.3±2 29.9±2.1 2±1.6
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.348753e-52 1.933822e-78 3.312064e-56 2.145736e-73 7.607715e-46
## [2,] 3.194105e-45 1.096806e-70 3.593552e-44 1.887984e-66 7.154394e-39
## [3,] 3.132262e-42 3.423701e-71 3.569080e-42 5.151096e-65 8.388832e-35
## [4,] 1.848977e-33 1.655005e-63 1.259538e-33 7.317435e-57 1.429635e-24
## [5,] 1.485701e-04 1.366447e-37 3.241202e-04 3.050189e-31 2.214493e-01
## Reward (M) Reward (SE) t-value d.o.f.
## Visual 42.5277777777778 2.58225850837387 16.4692177951459 179
## Cognitive 59.8194444444444 2.63485672377541 22.7031109147867 179
## Social 32.7527777777778 2.50978685597365 13.0500236304216 179
## Monetary 52.1722222222222 2.83766052035376 18.3856461504134 179
## p-value
## Visual 1.07375634043851e-37
## Cognitive 1.39102517737285e-54
## Social 8.75258165232007e-28
## Monetary 4.32975520437278e-43
## Stimulus Avg_Reward_Rating SE_Reward_Rating t_value df
## WantingV Visual 83.55556 1.945105 17.2512826 179
## WantingC Cognitive 94.40741 1.029483 43.1356333 179
## WantingS Social 80.62963 1.808622 16.9353382 179
## WantingM Monetary 92.03704 1.103134 38.1069303 179
## WantingN Neutral 52.51852 2.601233 0.9682018 179
## p_value
## WantingV 6.467603e-40
## WantingC 1.656193e-96
## WantingS 5.066978e-39
## WantingM 8.171054e-88
## WantingN 3.342499e-01
## Stimulus Avg_Reward_Rating SE_Reward_Rating t_value df
## LearningV Visual 73.58796 1.835468 12.8511969 179
## LearningC Cognitive 81.41204 1.416685 22.1729175 179
## LearningS Social 73.07870 1.962090 11.7623088 179
## LearningM Monetary 80.18519 1.393004 21.6691339 179
## LearningN Neutral 49.39815 2.388119 -0.2520192 179
## p_value
## LearningV 3.332946e-27
## LearningC 3.170293e-53
## LearningS 4.949371e-24
## LearningM 6.389283e-52
## LearningN 8.013153e-01
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Loading required namespace: GPArotation
## Experiment 3
Next, we tested whether the sensitivity to intrinsic reward is domain general. In other words, if a particular participant finds the visual reward rewarding, will they be more likely to find the cognitive and the social reward rewarding? Will they also find the monetary reward more rewarding? Next, we asked whether the ‘reward sensitivity’ score is related to affective aspects of mental health. That is, do people with high reward sensitivity experience better affective health?
## Warning in corrplot(M, method = "color", type = "full", tl.col = "black", :
## p.mat and corr may be not paired, their rownames and colnames are not totally
## same!
## R was not square, finding R from data
## $chisq
## [1] 378.1741
##
## $p.value
## [1] 1.651749e-32
##
## $df
## [1] 105
## [1] 0.04873468
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(lStim1))
## Overall MSA = 0.63
## MSA for each item =
## LikingV LikingC LikingS LikingM LikingN WantingV WantingC WantingS
## 0.59 0.73 0.71 0.70 0.53 0.54 0.84 0.44
## WantingM WantingN LearningV LearningC LearningS LearningM LearningN
## 0.69 0.60 0.68 0.68 0.67 0.57 0.55
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(lStim2))
## Overall MSA = 0.64
## MSA for each item =
## LikingV LikingC LikingS LikingM LikingN WantingV WantingC WantingS
## 0.65 0.70 0.76 0.74 0.65 0.56 0.65 0.51
## WantingM WantingN LearningV LearningC LearningS LearningM LearningN
## 0.54 0.60 0.64 0.68 0.60 0.68 0.54
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(lStim3))
## Overall MSA = 0.63
## MSA for each item =
## LikingV LikingC LikingS LikingM LikingN WantingV WantingC WantingS
## 0.69 0.66 0.66 0.68 0.64 0.59 0.59 0.55
## WantingM WantingN LearningV LearningC LearningS LearningM LearningN
## 0.57 0.58 0.65 0.68 0.63 0.63 0.64
## Warning in corrplot(M, method = "color", type = "full", tl.col = "black", :
## p.mat and corr may be not paired, their rownames and colnames are not totally
## same!
## R was not square, finding R from data
## $chisq
## [1] 547.8718
##
## $p.value
## [1] 4.097026e-61
##
## $df
## [1] 105
## [1] 0.03553312
## Warning in corrplot(M, method = "color", type = "full", tl.col = "black", :
## p.mat and corr may be not paired, their rownames and colnames are not totally
## same!
## R was not square, finding R from data
## $chisq
## [1] 616.8267
##
## $p.value
## [1] 1.903458e-73
##
## $df
## [1] 105
## [1] 0.02838084
##
## Call:
## lm(formula = mentalHealth_NoOutlier ~ gender + age + iq + edu +
## income + rewardSensitivity_NoOutlier, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.64524 -0.32459 0.06863 0.48245 1.35127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.542634 0.312276 1.738 0.08502 .
## gender -0.108929 0.141097 -0.772 0.44173
## age -0.007568 0.006328 -1.196 0.23425
## iq -0.026016 0.020446 -1.272 0.20586
## edu 0.022833 0.043300 0.527 0.59901
## income 0.029879 0.035639 0.838 0.40361
## rewardSensitivity_NoOutlier 0.243431 0.085830 2.836 0.00542 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6951 on 112 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.09895, Adjusted R-squared: 0.05068
## F-statistic: 2.05 on 6 and 112 DF, p-value: 0.06482
##
## Call:
## lm(formula = mentalHealth_NoOutlier ~ rewardSensitivity_NoOutlier,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7670 -0.3705 0.1169 0.4829 1.3736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13768 0.06514 2.114 0.0367 *
## rewardSensitivity_NoOutlier 0.22886 0.08510 2.689 0.0082 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7107 on 118 degrees of freedom
## Multiple R-squared: 0.05775, Adjusted R-squared: 0.04977
## F-statistic: 7.233 on 1 and 118 DF, p-value: 0.008195
## Stimulus Avg_Reward_Rating SE_Reward_Rating t_value df
## LearningV Visual 73.58796 1.835468 12.8511969 179
## LearningC Cognitive 81.41204 1.416685 22.1729175 179
## LearningS Social 73.07870 1.962090 11.7623088 179
## LearningM Monetary 80.18519 1.393004 21.6691339 179
## LearningN Neutral 49.39815 2.388119 -0.2520192 179
## p_value
## LearningV 3.332946e-27
## LearningC 3.170293e-53
## LearningS 4.949371e-24
## LearningM 6.389283e-52
## LearningN 8.013153e-01
## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = mentalHealth_NoOutlier ~ gender + age + iq + edu +
## income + rewardSensitivity_NoOutlier, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8261 -0.4640 0.1220 0.5133 1.2492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.311757 0.201388 -1.548 0.1237
## gender 0.016423 0.110991 0.148 0.8826
## age 0.008817 0.004091 2.155 0.0327 *
## iq -0.036977 0.017763 -2.082 0.0391 *
## edu 0.087861 0.036385 2.415 0.0170 *
## income 0.068980 0.027017 2.553 0.0117 *
## rewardSensitivity_NoOutlier 0.163267 0.067185 2.430 0.0163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6765 on 149 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1895, Adjusted R-squared: 0.1568
## F-statistic: 5.805 on 6 and 149 DF, p-value: 1.853e-05
##
## Call:
## lm(formula = mentalHealth_NoOutlier ~ rewardSensitivity_NoOutlier,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.92435 -0.50901 0.08923 0.58663 1.29426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15325 0.05843 2.623 0.00959 **
## rewardSensitivity_NoOutlier 0.15946 0.07125 2.238 0.02664 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7316 on 155 degrees of freedom
## Multiple R-squared: 0.0313, Adjusted R-squared: 0.02505
## F-statistic: 5.009 on 1 and 155 DF, p-value: 0.02664
## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = mentalHealth_NoOutlier ~ gender + age + iq + edu +
## income + rewardSensitivity_NoOutlier, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.78454 -0.42800 0.04683 0.59347 1.37850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.271961 0.294240 -0.924 0.35688
## gender 0.054157 0.124783 0.434 0.66493
## age 0.005028 0.005521 0.911 0.36402
## iq -0.021973 0.017978 -1.222 0.22361
## edu 0.073477 0.036664 2.004 0.04692 *
## income 0.031619 0.036288 0.871 0.38502
## rewardSensitivity_NoOutlier 0.250883 0.074558 3.365 0.00098 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7218 on 145 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1199, Adjusted R-squared: 0.08345
## F-statistic: 3.291 on 6 and 145 DF, p-value: 0.004577
##
## Call:
## lm(formula = mentalHealth_NoOutlier ~ rewardSensitivity_NoOutlier,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.90507 -0.49970 0.03716 0.57985 1.24335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10513 0.05942 1.769 0.07885 .
## rewardSensitivity_NoOutlier 0.25195 0.07501 3.359 0.00099 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7326 on 152 degrees of freedom
## Multiple R-squared: 0.06909, Adjusted R-squared: 0.06296
## F-statistic: 11.28 on 1 and 152 DF, p-value: 0.0009901
## RewardFactorCoefficients
## Experiment 1, Controlling for demographics 0.2434311
## Experiment 1, not controlling for demographics 0.2288592
## Experiment 2, Controlling for demographics 0.1632672
## Experiment 2, not controlling for demographics 0.1594557
## Experiment 3, Controlling for demographics 0.2508827
## Experiment 3, not controlling for demographics 0.2519450
## standard_errors
## Experiment 1, Controlling for demographics 0.08582972
## Experiment 1, not controlling for demographics 0.08509715
## Experiment 2, Controlling for demographics 0.06718457
## Experiment 2, not controlling for demographics 0.07124784
## Experiment 3, Controlling for demographics 0.07455848
## Experiment 3, not controlling for demographics 0.07501395
## degrees_of_freedom t_values
## Experiment 1, Controlling for demographics 112 2.836210
## Experiment 1, not controlling for demographics 118 2.689387
## Experiment 2, Controlling for demographics 149 2.430129
## Experiment 2, not controlling for demographics 155 2.238043
## Experiment 3, Controlling for demographics 145 3.364911
## Experiment 3, not controlling for demographics 152 3.358642
## p_values
## Experiment 1, Controlling for demographics 0.0054192351
## Experiment 1, not controlling for demographics 0.0081954399
## Experiment 2, Controlling for demographics 0.0162819949
## Experiment 2, not controlling for demographics 0.0266433912
## Experiment 3, Controlling for demographics 0.0009799862
## Experiment 3, not controlling for demographics 0.0009900977
## `geom_smooth()` using formula = 'y ~ x'